Enrichment Assessment of Multiple Virtual Screening Strategies for Toll‐Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set

Toll‐like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T‐helper 1‐polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll‐like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll‐like receptor 8 agonists. In this study, the performance of knowledge‐based pharmacophore, shape‐based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll‐like receptor 8 agonists. Prior structure–activity relationship knowledge was involved in knowledge‐based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge‐based pharmacophore. The benchmarking data set was generated from our recently developed ‘ mubd‐decoymaker ’ protocol. The enrichment assessment demonstrated a considerable performance through our selected three‐layer virtual screening strategy: knowledge‐based pharmacophore ( Phar1 ) screening, shape‐based 3D similarity search ( Q4_combo ), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large‐scale database screening and to discover novel toll‐like receptor 8 agonists.